標題: BAYESIAN INFERENCES OF LATENT CLASS MODELS WITH AN UNKNOWN NUMBER OF CLASSES
作者: Pan, Jia-Chiun
Huang, Guan-Hua
交大名義發表
統計學研究所
National Chiao Tung University
Institute of Statistics
關鍵字: categorical data;finite mixture model;label switching;reversible jump Markov chain Monte Carlo;sensitivity analysis;surrogate endpoint
公開日期: 1-十月-2014
摘要: This paper focuses on analyzing data collected in situations where investigators use multiple discrete indicators as surrogates, for example, a set of questionnaires. A very flexible latent class model is used for analysis. We propose a Bayesian framework to perform the joint estimation of the number of latent classes and model parameters. The proposed approach applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions. In the paper, we also develop a procedure for the unique labeling of the classes. We have carried out a detailed sensitivity analysis for various hyperparameter specifications, which leads us to make standard default recommendations for the choice of priors. The usefulness of the proposed method is demonstrated through computer simulations and a study on subtypes of schizophrenia using the Positive and Negative Syndrome Scale (PANSS).
URI: http://dx.doi.org/10.1007/S11336-013-9368-7
http://hdl.handle.net/11536/124148
ISSN: 0033-3123
DOI: 10.1007/S11336-013-9368-7
期刊: PSYCHOMETRIKA
Volume: 79
起始頁: 621
結束頁: 646
顯示於類別:期刊論文


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  1. 000346602200005.pdf